library(tidyverse)
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## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
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data()
BOD
## Time demand
## 1 1 8.3
## 2 2 10.3
## 3 3 19.0
## 4 4 16.0
## 5 5 15.6
## 6 7 19.8
library(gapminder)
gapminder
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rows
ca <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/ca.csv")
## Rows: 789 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): region, state, code, park_name, type
## dbl (2): visitors, year
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(ca)
## Rows: 789
## Columns: 7
## $ region <chr> "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", …
## $ state <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", …
## $ code <chr> "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHI…
## $ park_name <chr> "Channel Islands National Park", "Channel Islands National P…
## $ type <chr> "National Park", "National Park", "National Park", "National…
## $ visitors <dbl> 1200, 1500, 1600, 300, 15700, 31000, 33100, 32000, 24400, 31…
## $ year <dbl> 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, …
ggplot(ca, aes(x=year, y = visitors, color = park_name)) +
geom_point()
## More arguments in geoms
ggplot(ca, aes(x=year, y = visitors, color = park_name)) +
geom_point(alpha= 0.5) +
geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## customisation theme
ggplot(ca, aes(x=year, y = visitors, color = park_name)) +
geom_point(alpha= 0.5) +
geom_smooth() +
labs(x = "Year",
y = "Visitation",
title = "California National Park Visitation") +
theme_bw() +
theme(
legend.title = element_blank(),
axis.title.x = element_text(angle=45, size =20))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = ca, aes(x = year, y = visitors, color = code)) +
geom_point() +
facet_wrap(~ code, scales = "free") +
theme_bw()
library(gapminder)
gapminder %>%
ggplot(aes(x = log(gdpPercap), y = lifeExp, col = year, size = pop)) +
geom_point(alpha = 0.3) +
geom_smooth(method = lm) +
facet_wrap( ~ continent, scales = "free") +
theme_bw()
## `geom_smooth()` using formula 'y ~ x'
gapminder %>%
ggplot(aes( x= continent, y = lifeExp, col = continent)) +
geom_boxplot() +
geom_jitter(alpha = 0.3) +
labs(
x = "Life Expectancy",
y = "Continent",
colour = "Continent"
)
theme_bw()
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# Southeast US National Parks
se <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/se.csv")
## Rows: 453 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): region, state, code, park_name, type
## dbl (2): visitors, year
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
geom is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. You can use different geoms to plot the same data. To change the geom in your plot, change the geom function that you add to ggplot(). Let’s look at a few ways of viewing the distribution of annual visitation (visitors) for each park (park_name).
# flip
# add colour
# take out the legend
ggplot(data = se) +
geom_jitter(aes(x = park_name, y = visitors, color = park_name, alpha = 0.5)) +
coord_flip() +
theme_bw() +
theme(
legend.position = "none"
)
visit_16 <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/visit_16.csv")
## Rows: 17 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): region, state, code, park_name, type
## dbl (2): visitors, year
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
x <- ggplot(data = visit_16, aes(x = state, y = visitors, fill = park_name)) +
geom_bar(stat = "identity", position = "dodge") +
theme_bw()
x
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly(x)
plot <- ggplot(data = se) +
geom_jitter(aes(x = park_name, y = visitors, color = park_name, alpha = 0.5)) +
coord_flip() +
theme_bw() +
theme(
legend.position = "none"
)
ggplotly(plot)
After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.
Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi):
#ggsave(here("results","plot.png"), x, width = 15, height = 10)